Jennifer A. Brentrup, Craig E. Williamson, William Colom-Montero, Werner Eckert, Elvira de Eyto, Hans-Peter Grossart, Yannick Huot, Peter D. F. Isles, Lesley B. Knoll, Taylor H. Leach, Chris G. McBride, Don Pierson, Francesco Pomati, Jordan S. Read, Kevin C. Rose, Nihar R. Samal, Peter A. Staehr, Luke A. Winslow
- The use of high-frequency sensors on profiling buoys to investigate physical, chemical, and biological processes in lakes is
increasing rapidly. Profiling buoys with automated winches and sensors that collect high-frequency chlorophyll fluorescence
(ChlF) profiles in 11 lakes in the Global Lake Ecological Observatory Network (GLEON) allowed the study of the vertical
and temporal distribution of ChlF, including the formation of subsurface chlorophyll maxima (SSCM). The effectiveness of 3
methods for sampling phytoplankton distributions in lakes, including (1) manual profiles, (2) single-depth buoys, and (3)
profiling buoys were assessed. High-frequency ChlF surface data and profiles were compared to predictions from the
Plankton Ecology Group (PEG) model. The depth-integrated ChlF dynamics measured by the profiling buoy data revealed a
greater complexity that neither conventional sampling nor the generalized PEG model captured. Conventional sampling
techniques would have missed SSCM in 7 of 11 study lakes. Although surface-onlyThe use of high-frequency sensors on profiling buoys to investigate physical, chemical, and biological processes in lakes is
increasing rapidly. Profiling buoys with automated winches and sensors that collect high-frequency chlorophyll fluorescence
(ChlF) profiles in 11 lakes in the Global Lake Ecological Observatory Network (GLEON) allowed the study of the vertical
and temporal distribution of ChlF, including the formation of subsurface chlorophyll maxima (SSCM). The effectiveness of 3
methods for sampling phytoplankton distributions in lakes, including (1) manual profiles, (2) single-depth buoys, and (3)
profiling buoys were assessed. High-frequency ChlF surface data and profiles were compared to predictions from the
Plankton Ecology Group (PEG) model. The depth-integrated ChlF dynamics measured by the profiling buoy data revealed a
greater complexity that neither conventional sampling nor the generalized PEG model captured. Conventional sampling
techniques would have missed SSCM in 7 of 11 study lakes. Although surface-only ChlF data underestimated average water
column ChlF, at times by nearly 2-fold in 4 of the lakes, overall there was a remarkable similarity between surface and mean
water column data. Contrary to the PEG model’s proposed negligible role for physical control of phytoplankton during the
growing season, thermal structure and light availability were closely associated with ChlF seasonal depth distribution. Thus,
an extension of the PEG model is proposed, with a new conceptual framework that explicitly includes physical metrics to
better predict SSCM formation in lakes and highlight when profiling buoys are especially informative.…
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MetadatenAuthor details: | Jennifer A. Brentrup, Craig E. Williamson, William Colom-Montero, Werner Eckert, Elvira de Eyto, Hans-Peter GrossartORCiDGND, Yannick Huot, Peter D. F. Isles, Lesley B. KnollORCiD, Taylor H. Leach, Chris G. McBride, Don Pierson, Francesco Pomati, Jordan S. Read, Kevin C. Rose, Nihar R. Samal, Peter A. Staehr, Luke A. Winslow |
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DOI: | https://doi.org/10.5268/IW-6.4.890 |
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ISSN: | 2044-2041 |
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ISSN: | 2044-205X |
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Title of parent work (English): | Inland waters : journal of the International Society of Limnology |
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Publisher: | Freshwater Biological Association |
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Place of publishing: | Ambleside |
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Publication type: | Article |
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Language: | English |
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Year of first publication: | 2016 |
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Publication year: | 2016 |
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Release date: | 2020/03/22 |
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Tag: | Global Lake Ecological Observatory Network (GLEON); PEG model; chlorophyll fluorescence; high-frequency sensors; phytoplankton; profiling buoys; subsurface chlorophyll maximum |
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Volume: | 6 |
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Number of pages: | 16 |
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First page: | 565 |
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Last Page: | 580 |
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Funding institution: | core Marine Institute funding; Bay of Plenty Regional Council; GLEON; Miami University; NSF DEB IGERT grant [0903560]; US Geological Survey Center for Integrated Data Analytics; NSF DEB grant [0822700]; NTL-LTER program; Danish Council for Independent Research Natural Sciences [10-085238] |
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Organizational units: | Mathematisch-Naturwissenschaftliche Fakultät / Institut für Biochemie und Biologie |
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Peer review: | Referiert |
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